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reduced memory requirements in opencv_stitching

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This commit is contained in:
Alexey Spizhevoy 2011-05-18 14:52:23 +00:00
parent 71ca501bb6
commit 4567b4326b
6 changed files with 34 additions and 40 deletions
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2
modules/stitching/autocalib.cpp
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@ -56,7 +56,7 @@ double estimateFocal(const vector<Mat> &images, const vector<ImageFeatures> &/*f
}
}
if (focals.size() + 1 >= images.size())
if (focals.size() >= 2 * (num_images - 1))
{
nth_element(focals.begin(), focals.end(), focals.begin() + focals.size()/2);
return focals[focals.size()/2];

30
modules/stitching/blenders.cpp
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@ -133,19 +133,20 @@ Point MultiBandBlender::blend(const vector<Mat> &src, const vector<Point> &corne
int left = corners[img_idx].x - dst_roi.x;
int right = dst_roi.br().x - corners[img_idx].x - src[img_idx].cols;
Mat big_src;
copyMakeBorder(src[img_idx], big_src, top, bottom, left, right, BORDER_REFLECT);
vector<Mat> src_pyr_gauss;
vector<Mat> src_pyr_gauss(num_bands_ + 1);
copyMakeBorder(src[img_idx], src_pyr_gauss[0], top, bottom, left, right, BORDER_REFLECT);
for (int i = 0; i < num_bands_; ++i)
pyrDown(src_pyr_gauss[i], src_pyr_gauss[i + 1]);
vector<Mat> src_pyr_laplace;
createGaussPyr(big_src, num_bands_, src_pyr_gauss);
createLaplacePyr(src_pyr_gauss, src_pyr_laplace);
Mat big_mask;
copyMakeBorder(masks[img_idx], big_mask, top, bottom, left, right, BORDER_CONSTANT);
Mat weight_map;
big_mask.convertTo(weight_map, CV_32F, 1./255.);
vector<Mat> weight_pyr_gauss;
createGaussPyr(weight_map, num_bands_, weight_pyr_gauss);
vector<Mat> weight_pyr_gauss(num_bands_ + 1);
Mat mask_f;
masks[img_idx].convertTo(mask_f, CV_32F, 1./255.);
copyMakeBorder(mask_f, weight_pyr_gauss[0], top, bottom, left, right, BORDER_CONSTANT);
for (int i = 0; i < num_bands_; ++i)
pyrDown(weight_pyr_gauss[i], weight_pyr_gauss[i + 1]);
for (int band_idx = 0; band_idx <= num_bands_; ++band_idx)
{
@ -286,15 +287,6 @@ void createWeightMap(const Mat &mask, float sharpness, Mat &weight)
}
void createGaussPyr(const Mat &img, int num_layers, vector<Mat> &pyr)
{
pyr.resize(num_layers + 1);
pyr[0] = img.clone();
for (int i = 0; i < num_layers; ++i)
pyrDown(pyr[i], pyr[i + 1]);
}
void createLaplacePyr(const vector<Mat> &pyr_gauss, vector<Mat> &pyr_laplace)
{
if (pyr_gauss.size() == 0)

2
modules/stitching/blenders.hpp
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@ -63,8 +63,6 @@ void normalize(const cv::Mat& weight, cv::Mat& src);
void createWeightMap(const cv::Mat& mask, float sharpness, cv::Mat& weight);
void createGaussPyr(const cv::Mat& img, int num_layers, std::vector<cv::Mat>& pyr);
void createLaplacePyr(const std::vector<cv::Mat>& pyr_gauss, std::vector<cv::Mat>& pyr_laplace);
// Restores source image in-place. Result will be stored in pyr[0].

22
modules/stitching/main.cpp
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@ -35,8 +35,8 @@ int main(int argc, char* argv[])
vector<string> img_names;
vector<Mat> images;
bool trygpu = true;
double work_megapix = -1;
double compose_megapix = -1;
double work_megapix = 1;
double compose_megapix = 1;
int ba_space = BundleAdjuster::FOCAL_RAY_SPACE;
float conf_thresh = 1.f;
bool wave_correct = true;
@ -47,8 +47,8 @@ int main(int argc, char* argv[])
int blend_type = Blender::MULTI_BAND;
string result_name = "result.png";
double work_scale = 1, compose_scale = 1;
bool is_work_scale_set = false, is_compose_scale_set = true;
double work_scale, compose_scale;
bool is_work_scale_set = false, is_compose_scale_set = false;
if (argc == 1)
{
@ -85,7 +85,6 @@ int main(int argc, char* argv[])
else if (string(argv[i]) == "--compose_megapix")
{
compose_megapix = atof(argv[i + 1]);
is_compose_scale_set = false;
i++;
}
else if (string(argv[i]) == "--result")
@ -195,7 +194,7 @@ int main(int argc, char* argv[])
{
if (!is_work_scale_set)
{
work_scale = min(1.0, sqrt(work_megapix * 1000000 / full_img.size().area()));
work_scale = min(1.0, sqrt(work_megapix * 1e6 / full_img.size().area()));
is_work_scale_set = true;
}
Mat img;
@ -224,7 +223,11 @@ int main(int argc, char* argv[])
matcher = BestOf2NearestMatcher(true, match_conf);
matcher(images, features, pairwise_matches);
leaveBiggestComponent(images, features, pairwise_matches, conf_thresh);
vector<int> indices = leaveBiggestComponent(images, features, pairwise_matches, conf_thresh);
vector<string> img_names_subset;
for (size_t i = 0; i < indices.size(); ++i)
img_names_subset.push_back(img_names[indices[i]]);
img_names = img_names_subset;
num_images = static_cast<int>(images.size());
if (num_images < 2)
@ -271,14 +274,15 @@ int main(int argc, char* argv[])
nth_element(focals.begin(), focals.end(), focals.begin() + focals.size() / 2);
float camera_focal = static_cast<float>(focals[focals.size() / 2]);
if (work_megapix > 0 || compose_megapix > 0)
if ((work_megapix > 0 || compose_megapix > 0)
&& abs(work_megapix - compose_megapix) > 1e-3)
{
for (int i = 0; i < num_images; ++i)
{
Mat full_img = imread(img_names[i]);
if (!is_compose_scale_set)
{
compose_scale = min(1.0, sqrt(compose_megapix * 1000000 / full_img.size().area()));
compose_scale = min(1.0, sqrt(compose_megapix * 1e6 / full_img.size().area()));
is_compose_scale_set = true;
}
Mat img;

14
modules/stitching/motion_estimators.cpp
View File

@ -328,8 +328,7 @@ void waveCorrect(vector<Mat> &rmats)
SVD svd;
svd(cov, SVD::FULL_UV);
svd.vt.row(2).copyTo(r1);
if (determinant(svd.vt) < 0)
r1 *= -1;
if (determinant(svd.vt) < 0) r1 *= -1;
Mat avgz = Mat::zeros(3, 1, CV_32F);
for (size_t i = 0; i < rmats.size(); ++i)
@ -338,8 +337,7 @@ void waveCorrect(vector<Mat> &rmats)
normalize(r0, r0);
r1.cross(r0).copyTo(r2);
if (determinant(R) < 0)
R *= -1;
if (determinant(R) < 0) R *= -1;
for (size_t i = 0; i < rmats.size(); ++i)
rmats[i] = R * rmats[i];
@ -348,8 +346,8 @@ void waveCorrect(vector<Mat> &rmats)
//////////////////////////////////////////////////////////////////////////////
void leaveBiggestComponent(vector<Mat> &images, vector<ImageFeatures> &features,
vector<MatchesInfo> &pairwise_matches, float conf_threshold)
vector<int> leaveBiggestComponent(vector<Mat> &images, vector<ImageFeatures> &features,
vector<MatchesInfo> &pairwise_matches, float conf_threshold)
{
const int num_images = static_cast<int>(images.size());
@ -393,7 +391,7 @@ void leaveBiggestComponent(vector<Mat> &images, vector<ImageFeatures> &features,
}
if (static_cast<int>(images_subset.size()) == num_images)
return;
return indices;
LOG("Removed some images, because can't match them: (");
LOG(indices_removed[0]);
@ -403,6 +401,8 @@ void leaveBiggestComponent(vector<Mat> &images, vector<ImageFeatures> &features,
images = images_subset;
features = features_subset;
pairwise_matches = pairwise_matches_subset;
return indices;
}

4
modules/stitching/motion_estimators.hpp
View File

@ -83,8 +83,8 @@ void waveCorrect(std::vector<cv::Mat> &rmats);
//////////////////////////////////////////////////////////////////////////////
// Auxiliary functions
void leaveBiggestComponent(std::vector<cv::Mat> &images, std::vector<ImageFeatures> &features,
std::vector<MatchesInfo> &pairwise_matches, float conf_threshold);
std::vector<int> leaveBiggestComponent(std::vector<cv::Mat> &images, std::vector<ImageFeatures> &features,
std::vector<MatchesInfo> &pairwise_matches, float conf_threshold);
void findMaxSpanningTree(int num_images, const std::vector<MatchesInfo> &pairwise_matches,
Graph &span_tree, std::vector<int> &centers);